Characterizing Compound Inland Flooding Mechanisms and Risks in North America Under Climate Change
Bibliographic record
Abstract
Abstract Compound inland flooding (CIF) arises from the concurrent interaction of multiple hydrometeorological drivers. In this study, we characterize key CIF events across North America, including two preconditioned events, rain‐on‐snow (ROS) and saturation excess flooding (SEF) for historical baseline conditions and global warming levels of 1.5, 2, and 4°C relative to the preindustrial level. Utilizing the high emission climate scenario (RCP8.5) from CanRCM4‐LE with 50 members, the frequency and seasonality of compound events, along with the probability of these events leading to heavy runoff, and the relative role of external forcing and internal climate variability are assessed. We convert the identified hazards into risk levels by integrating them with exposure and vulnerability components. The results suggest that as global temperatures increase, the overall role of ROS events in causing significant runoff is projected to decrease compared to individual heavy rainfall. Concurrently, the impact of SEF occurrences is projected to become more pronounced. The signal‐to‐noise ratio highlights a high‐confidence change signal for CIF events; however, uncertainty related to internal climate variability in future projections of joint probability with heavy runoff is more pronounced. These results underscore the need to consider compound mechanisms, dynamics, and risks associated with CIFs within systematic approaches to flood risk management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".